Grasp Recognition for Programming by Demonstration
Grasp Recognition for Programming by Demonstration
复制标题
通过演示掌握认知编程
DOI：
10.1109/robot.2005.1570207
复制
发表时间：
2005
期刊：
影响因子：
--
通讯作者：
D. Kragic
中科院分区：
文献类型：
--
作者：
S. Ekvall;D. Kragic
对灵活且可重新编程的机器人的需求增加了通过演示系统进行编程的需求。在本文中，在演示编程框架中考虑了抓取识别。提出并评估了三种抓握识别方法。第一种方法使用隐马尔可夫模型对抓取序列期间的手部姿势序列进行建模，而第二种方法则依赖于手部轨迹和手部旋转。第三种方法是混合方法，其中前两种方法并行有效。特别的贡献是所有方法都依赖于抓握顺序，而不仅仅是手的最终姿势。这有助于在抓取完成之前进行抓取识别。此外，通过分析整个序列而不仅仅是最终掌握，决策基于更多信息，并提高了整个系统的稳健性。实验结果表明，手臂轨迹和最终手部姿势都为抓取分类提供了重要信息。通过将它们结合起来，提高了整个系统的识别率。
The demand for flexible and re-programmable robots has increased the need for programming by demonstration systems. In this paper, grasp recognition is considered in a programming by demonstration framework. Three methods for grasp recognition are presented and evaluated. The first method uses Hidden Markov Models to model the hand posture sequence during the grasp sequence, while the second method relies on the hand trajectory and hand rotation. The third method is a hybrid method, in which both the first two methods are active in parallel. The particular contribution is that all methods rely on the grasp sequence and not just the final posture of the hand. This facilitates grasp recognition before the grasp is completed. Also, by analyzing the entire sequence and not just the final grasp, the decision is based on more information and increased robustness of the overall system is achieved. The experimental results show that both arm trajectory and final hand posture provide important information for grasp classification. By combining them, the recognition rate of the overall system is increased.